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  <doc>
    <id>9588</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2328</pageFirst>
    <pageLast>2335</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
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    <title language="eng">An Almost Feasible Sequential Linear Programming Algorithm</title>
    <abstract language="eng">This paper proposes an almost feasible Sequential Linear Programming (afSLP) algorithm. In the first part, the practical limitations of previously proposed Feasible Sequential Linear Programming (FSLP) methods are discussed along with illustrative examples. Then, we present a generalization of FSLP based on a tolerance-tube method that addresses the shortcomings of FSLP. The proposed algorithm afSLP consists of two phases. Phase I starts from random infeasible points and iterates towards a relaxation of the feasible set. Once the tolerance-tube around the feasible set is reached, phase II is started and all future iterates are kept within the tolerance-tube. The novel method includes enhancements to the originally proposed tolerance-tube method that are necessary for global convergence. afSLP is shown to outperform FSLP and the state-of-the-art solver IPOPT on a SCARA robot optimization problem.</abstract>
    <parentTitle language="eng">2024 European Control Conference (ECC), Stockholm, Sweden</parentTitle>
    <identifier type="arxiv">2401.13840</identifier>
    <identifier type="doi">10.23919/ECC64448.2024.10590864</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2024-02-29</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>David Kiessling</author>
    <submitter>Charlie Vanaret</submitter>
    <author>Charlie Vanaret</author>
    <author>Alejandro Astudillo</author>
    <author>Wilm Decré</author>
    <author>Jan Swevers</author>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="persons" number="vanaret">Vanaret, Charlie William</collection>
    <collection role="projects" number="MODAL-EnergyLab">MODAL-EnergyLab</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
</export-example>
